A key research issue in activity recognition in real-world applications, such as in intelligent transportation systems (ITS), is to automatically learn robust models of activities that require minimal human training. In this paper, we contribute a novel approach for learning sequenced spatiotemporal activities in outdoor traffic intersections. Concretely, by representing the activities as sequences of actions, we contribute a semisupervised learning algorithm that learns activities as complete stochastic context-free grammars (SCFGs), namely, the grammar structure and the parameters. Our approach has been implemented and tested on real-world scenes, and we present experimental results of the grammar learning and activity recognition applied to datacollection and traffic monitoring applications using video data.
|Original language||English (US)|
|Number of pages||11|
|Journal||IEEE Transactions on Intelligent Transportation Systems|
|State||Published - Dec 2009|
Bibliographical noteFunding Information:
Manuscript received September 15, 2008; revised March 27, 2009. First published July 17, 2009; current version published December 3, 2009. This work was supported in part by the National Science Foundation under Grant IIS-0219863, Grant CNS-0224363, Grant CNS-0324864, Grant CNS-0420836, Grant IIP-0443945, Grant IIP-0726109, and Grant CNS-0708344, by the ITS Institute at the University of Minnesota, and by the Minnesota Department of Transportation.
- Context-free grammars
- Intelligent transportation system (ITS) applications
- Machine learning
- Vehicle tracking
- Video analysis